Off Shelf Alerting and Sales Velocity

Let's talk about sales velocity.  Off shelf-detection algorithms are based on identifying a significant gap between what you expected to sell and what you actually sold (at a product-store level).  The basic system I described in "Off-Shelf Alerting tools are simple" will call out an alert when, for a period of time, you sold nothing, but expected to sell at least 5 units.  So how long should it take the average product at the average store to sell 5 units?  Rather longer than you might think.

The majority of supermarket grocery products sell less than 1 unit per store per week.  I have seen this borne out repeatedly in practice and it’s supported by other studies.  While I expect this feels wrong to you - you probably buy enough milk every week for this to seem wrong - it's real and has a huge impact on the value of off-shelf alerting, so please bear with me.

I can't share real data with you but I can generate something instructive from 2 key facts

Now, with a little math-magic I can figure out the average sales velocity for each 1% of the product range so that both these facts hold true.   The following results aren’t exact of course – some stores sell more than others, some have more products, some hold more closely to the Pareto assumption  but the results are representative and more importantly, instructive.

This is what Unit Velocity (unit sales per store per week) looks like.  There are a handful of very high velocity items (milk, some produce, fresh goods, soft-drinks, water, heavily-promoted items), but the vast majority clearly sell fewer, far fewer, than 10 a week. 

Let’s look at that long tail in a little more detail:  The bottom 90% of products sell less than 3.5 units per week and over 50% of products sell less than 1 unit per store per week.


What does this mean for off-shelf detection algorithms? 

Remember, the basic off-shelf decision rule is that we will call out an alert when you should have sold at least 5 units but actually sold none. With that in mind:

  • There are a few (very, very high-volume products) that could conceivably generate alerts on an hourly, intra-day basis.  My guess though is that if you really are out of milk, your customers may let you know faster than the algorithm can spit out a report.
  • Very few products have enough velocity even on a daily basis that 1 day of zero-sales is enough to flag an alert.
  • The “average product” will take weeks of zero sales before you can call an alert with any confidence.
  • Very low volume products may take so long it’s really not worth the effort in trying – thank goodness you haven’t lost too many sales waiting for that alert.

Now, this does not negate the value of off-shelf detection tools but it does help put things into context as to what they can (and can’t) do.

  • Being able to generate alerts slightly earlier in the day is of very little value to you.  Things just don’t change that fast.
  • Hourly data is of very little value other than for a handful of very high-velocity items (and I strongly suspect these will self-heal at store level before you can even plan any kind of external intervention). 
  • Daily data is useful for the top 40%-50% of products, for all others weekly data is fine.
  • It should come as no surprise that off-shelf detection algorithms generate far fewer alerts than you find when you do a physical audit: many of the off-shelf events get fixed by store operations before an off-shelf detection algorithm is capable of spotting them.

This post is the eighth in a series on On-Shelf Availability.